A Real–time Adaptive Sampling Method for Field Mapping in Patchy, Heterogeneous Environments
Bibliographic record
Abstract
Many environmental studies require detailed maps describing the spatial distribution of various environmental characteristics. These distributions tend to be ‘patchy’; that is, their structure and their relationships vary from place to place according to the influences of the local setting. We present a simple sampling method that adapts the sample spacing on a point–by–point basis as the data are collected. The resulting sample is denser in areas of higher variability and sparser in more ‘well–behaved’ areas, and is collected in a single traverse of the transect. It uses a combination of simple fuzzy functions representing the concepts ‘too close’ and ‘too far’ that are adaptively parameterized based on current measurements. The adaptive sampler produced better representations for 47% of simulated reference transects than uniform or random samples of the same size under perfect measurement conditions, increasing to best performance for 71% of the transects when measurement error was severe with only minimal increase in sampling density. The rapid calculations can be easily incorporated into real–time data acquisition software, and the method may be extended to achieve some type of compromise when faced with the need to sample multiple simultaneous variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".